CN112615387A - Energy storage capacity configuration method and device, computer equipment and readable storage medium - Google Patents
Energy storage capacity configuration method and device, computer equipment and readable storage medium Download PDFInfo
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- CN112615387A CN112615387A CN202011519177.4A CN202011519177A CN112615387A CN 112615387 A CN112615387 A CN 112615387A CN 202011519177 A CN202011519177 A CN 202011519177A CN 112615387 A CN112615387 A CN 112615387A
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/28—Arrangements for balancing of the load in a network by storage of energy
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J15/00—Systems for storing electric energy
- H02J15/008—Systems for storing electric energy using hydrogen as energy vector
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/008—Circuit arrangements for ac mains or ac distribution networks involving trading of energy or energy transmission rights
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
- H02J3/381—Dispersed generators
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
- H02J2300/20—The dispersed energy generation being of renewable origin
- H02J2300/28—The renewable source being wind energy
Abstract
The application relates to an energy storage capacity configuration method and device, computer equipment and a readable storage medium. In the energy storage capacity configuration method, a comprehensive profit optimization function of the energy storage system is obtained according to the profit of the energy storage system operated at a typical cycle for supplying power to a power grid, the profit of supplying hydrogen to a hydrogen load, the profit of supplying power to a load of an offshore oil field, the profit of supplying hydrogen to a gas turbine in the offshore oil field and the construction and maintenance cost. The capacity allocation optimization model comprises a comprehensive profit optimization function and capacity allocation constraint conditions. The energy storage capacity configuration method obtains the capacity configuration information of the energy storage system by solving the capacity configuration optimization model of the energy storage system, and configures the capacity in the energy storage system according to the capacity configuration information so as to maximize the benefit. In addition, the energy storage capacity configuration method fully considers the influences of offshore oil field load and a gas turbine power part in an offshore oil field, and the capacity configuration of the obtained energy storage system is better.
Description
Technical Field
The present application relates to the field of power technologies, and in particular, to a method and an apparatus for configuring energy storage capacity, a computer device, and a readable storage medium.
Background
The offshore wind power-hydrogen energy system is a grid-connected comprehensive energy system which takes an offshore wind power cluster as a core and a lightweight hydrogen system for preparing and storing hydrogen of an offshore platform as a flexible adjusting unit and can continuously provide electric energy/hydrogen energy for offshore industry nearby. After flexible regulation and control, offshore wind energy resources are utilized to the maximum on site, and abundant wind energy can be injected into an onshore power grid in a friendly mode. How to carry out capacity allocation in an offshore wind power-hydrogen energy system to maximize the income is an urgent problem to be solved.
Disclosure of Invention
Based on this, it is necessary to provide an energy storage capacity configuration method, apparatus, computer device and readable storage medium for the problem of how to perform capacity configuration in an offshore wind power-hydrogen energy system to maximize the profit.
An energy storage capacity configuration method, comprising:
obtaining power supply income to a power grid, hydrogen supply income to a hydrogen load, power supply income to a load of an offshore oil field, hydrogen supply income to a gas turbine in the offshore oil field and construction and maintenance cost of the energy storage system in typical operation, and obtaining a comprehensive income optimization function of the energy storage system according to the power supply income to the power grid, the hydrogen supply income to the hydrogen load, the power supply income to the load of the offshore oil field, the hydrogen supply income to the gas turbine in the offshore oil field and the construction and maintenance cost of the energy storage system.
And acquiring the capacity configuration constraint condition of the energy storage system. And obtaining a capacity configuration optimization model of the energy storage system according to the comprehensive profit optimization function and the capacity configuration constraint condition.
And solving a capacity configuration optimization model of the energy storage system by taking the capacity configuration information as a target to obtain the capacity configuration information of the energy storage system.
And configuring the capacity in the energy storage system according to the capacity configuration information.
In one embodiment, the step of obtaining a comprehensive profit optimization function for the energy storage system based on the power supply profit to the grid, the hydrogen supply profit to the hydrogen load, the power supply profit to the offshore field load, the hydrogen supply profit to the gas turbine in the offshore field, and the construction and maintenance costs of the energy storage system for a typical week of operation comprises:
and obtaining the annual average operation income of the energy storage system according to the income of supplying power to the power grid, the income of supplying hydrogen to the hydrogen load, the income of supplying power to the offshore oil field load and the income of supplying hydrogen to the gas turbine in the offshore oil field in the typical weekly operation.
And obtaining the comprehensive income optimization function according to the annual average operation income and the construction and maintenance cost.
In one embodiment, the step of deriving an annual average operating yield of the energy storage system from the power supply to the grid, the hydrogen supply to the hydrogen load, the power supply to the offshore field load and the hydrogen supply to the gas turbine in the offshore field operating at a typical week comprises:
obtaining maximum operation income according to the power supply income to the power grid, the hydrogen supply income to the hydrogen load, the power supply income to the offshore oil field load and the hydrogen supply income to the gas turbine in the offshore oil field when the energy storage system operates in a typical period.
And obtaining the annual average operation income according to the maximum operation income.
In one embodiment, the capacity configuration constraints include: the system comprises an offshore wind power active power balance constraint formula, an offshore oil field load active power balance constraint formula, a gas turbine maximum output constraint formula, a gas turbine minimum output constraint formula, a hot standby constraint formula, a gas turbine fuel constraint formula, an electrolysis cell power constraint formula, a fuel cell power constraint formula, an electrolysis cell and fuel cell efficiency constraint formula, a hydrogen load demand constraint formula or a gas storage tank volume constraint formula.
In one embodiment, the energy storage capacity configuration method further includes:
and obtaining the power supply income for the power grid according to the direct on-line power of the offshore wind power, the power generation on-line power of the fuel cell, the on-line electricity price and the transmission loss coefficient.
In one embodiment, the energy storage capacity configuration method further includes:
and obtaining the hydrogen supply income to the hydrogen load according to the hydrogen supply amount of the hydrogen load and the hydrogen selling price.
In one embodiment, the energy storage capacity configuration method further includes:
and obtaining the power supply income for the offshore oilfield load according to the offshore wind power supply offshore load power, the fuel cell supply offshore load power and the supply offshore load price.
In one embodiment, the energy storage capacity configuration method further includes:
and obtaining the hydrogen supply income of the gas turbine in the offshore oil field according to the hydrogen supply amount of the gas turbine, the selling price of the natural gas and the ratio of the unit volume heat value of the hydrogen to the unit volume heat value of the natural gas.
In one embodiment, the step of solving the capacity configuration optimization model of the energy storage system with the capacity configuration information as a target to obtain the capacity configuration information of the energy storage system includes:
and solving a capacity configuration optimization model of the energy storage system by adopting a particle swarm optimization with capacity configuration information as a target to obtain optimal capacity configuration information of the energy storage system, and taking the optimal capacity configuration information as the capacity configuration information of the energy storage system.
An energy storage capacity configuration device comprises a first acquisition module, a second acquisition module, a solving module and a configuration module.
The first acquisition module is used for acquiring the energy storage system's income to the power supply of electric wire netting, to hydrogen load hydrogen supply income, to the profit of offshore oil field load power supply, to the gas turbine in the offshore oil field hydrogen supply income and construction maintenance cost, and according to energy storage system's income to the power supply of electric wire netting, to hydrogen load hydrogen supply income, to offshore oil field load power supply income, to the gas turbine in the offshore oil field hydrogen supply income and construction maintenance cost obtain energy storage system's comprehensive income optimization function.
The second obtaining module is used for obtaining a capacity configuration constraint condition of the energy storage system and obtaining a capacity configuration optimization model of the energy storage system according to the comprehensive profit optimization function and the capacity configuration constraint condition.
The solving module is used for solving the capacity configuration optimization model of the energy storage system by taking the capacity configuration information as a target to obtain the capacity configuration information of the energy storage system.
The configuration module is used for configuring the capacity of the energy storage system according to the capacity configuration information.
A computer device comprising a memory, a processor, said memory having stored thereon a computer program operable on the processor, said processor when executing said computer program implementing the steps of the energy storage capacity configuration method according to any of the embodiments described above.
A readable storage medium, on which a computer program is stored, which computer program, when being executed by a processor, carries out the steps of the energy storage capacity configuration method according to any of the embodiments described above.
According to the energy storage capacity configuration method provided by the embodiment of the application, the comprehensive profit optimization function of the energy storage system is obtained according to the profit of the energy storage system running at a typical period from power supply to a power grid, the profit of hydrogen load supply, the profit of power supply to a load of an offshore oil field, the profit of hydrogen supply to a gas turbine in the offshore oil field and the construction and maintenance cost. The capacity allocation optimization model includes the synthetic revenue optimization function and the capacity allocation constraints. The energy storage capacity configuration method obtains the capacity configuration information of the energy storage system by solving a capacity configuration optimization model of the energy storage system, and configures the capacity in the energy storage system according to the capacity configuration information so as to maximize the benefit. In addition, the energy storage capacity configuration method fully considers the influences of offshore oil field load and a gas turbine power part in an offshore oil field, and the capacity configuration of the energy storage system is better.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments or the conventional technologies of the present application, the drawings used in the descriptions of the embodiments or the conventional technologies will be briefly introduced below, it is obvious that the drawings in the following descriptions are only some embodiments of the present application, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a flow chart of the energy storage capacity configuration method provided in an embodiment of the present application;
fig. 2 is a flowchart of the energy storage capacity configuration method provided in an embodiment of the present application;
fig. 3 is a flowchart of the energy storage capacity configuration method provided in an embodiment of the present application;
FIG. 4 is a flow chart of the particle swarm algorithm provided in one embodiment of the present application;
FIG. 5 is a diagram illustrating hydrogen energy distribution for the energy storage capacity allocation method provided in an embodiment of the present application;
fig. 6 is a parameter diagram for configuration optimization of the energy storage capacity configuration method provided in an embodiment of the present application.
Detailed Description
In order to make the aforementioned objects, features and advantages of the present application more comprehensible, embodiments accompanying the present application are described in detail below with reference to the accompanying drawings. In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present application. This application is capable of embodiments in many different forms than those described herein and those skilled in the art will be able to make similar modifications without departing from the spirit of the application and it is therefore not intended to be limited to the embodiments disclosed below.
The numbering of the components as such, e.g., "first", "second", etc., is used herein for the purpose of describing the objects only, and does not have any sequential or technical meaning. The term "connected" and "coupled" when used in this application, unless otherwise indicated, includes both direct and indirect connections (couplings). In the description of the present application, it is to be understood that the terms "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", "clockwise", "counterclockwise", and the like, indicate orientations or positional relationships based on those shown in the drawings, and are used only for convenience in describing the present application and for simplicity in description, and do not indicate or imply that the devices or elements referred to must have a particular orientation, be constructed in a particular orientation, and be operated, and thus, are not to be considered as limiting the present application.
In this application, unless expressly stated or limited otherwise, the first feature "on" or "under" the second feature may be directly contacting the first and second features or indirectly contacting the first and second features through intervening media. Also, a first feature "on," "over," and "above" a second feature may be directly or diagonally above the second feature, or may simply indicate that the first feature is at a higher level than the second feature. A first feature being "under," "below," and "beneath" a second feature may be directly under or obliquely under the first feature, or may simply mean that the first feature is at a lesser elevation than the second feature.
Referring to fig. 1, an embodiment of the present application provides a method for configuring energy storage capacity, including:
s100, obtaining power supply income to a power grid, hydrogen supply income to a hydrogen load, power supply income to a load of an offshore oil field, hydrogen supply income to a gas turbine in the offshore oil field and construction and maintenance cost of the energy storage system in typical operation, and obtaining a comprehensive income optimization function of the energy storage system according to the power supply income to the power grid, the hydrogen supply income to the hydrogen load, the power supply income to the load of the offshore oil field, the hydrogen supply income to the gas turbine in the offshore oil field and the construction and maintenance cost of the energy storage system.
The energy storage system comprises an offshore wind power-hydrogen energy system. The offshore wind power-hydrogen energy system comprises an offshore wind power system, an electrolytic cell, a hydrogen storage tank, a fuel cell, a gas turbine, an offshore load and other equipment.
Offshore wind power systems are used to convert wind energy into electrical energy. The electrolytic cell is used for converting electric energy into hydrogen energy. The hydrogen storage tank is used for storing hydrogen energy. Fuel cells are used to convert hydrogen energy into electrical energy. Gas turbines are used to convert hydrogen to electrical energy. The offshore load uses electric energy to do work.
The electric energy generated by the offshore wind power system is directly connected to a power grid and output to an electrolytic cell or an offshore load.
The hydrogen energy generated by the electrolysis cell can be stored in a hydrogen storage tank, output to a hydrogen load, and output to a fuel cell or a gas turbine. The hydrogen load refers to the direct sale of hydrogen gas through a pipeline, and may be other loads that consume hydrogen gas (excluding fuel cells and gas turbines).
The income from power supply to the power grid refers to the income from a wind power system or a fuel cell in the energy storage system to power supply to the power grid. The income of supplying hydrogen to the hydrogen load refers to the income of supplying hydrogen to the hydrogen load by an electrolytic cell or a hydrogen storage tank in the energy storage system. The income of the power supply to the offshore oilfield load refers to the income of the wind power system or the fuel cell in the energy storage system for supplying power to the power grid. The income from supplying hydrogen to the gas turbine in the offshore oil field refers to the income from the hydrogen storage tank to supplying hydrogen to the gas turbine.
In one embodiment, the construction and maintenance costs include an annual average investment cost and an annual average operational maintenance cost for the energy storage system.
The typical week's wind power output (offshore wind power output) and the typical week's offshore field load curve can represent the annual wind power output and offshore field load condition, respectively. A typical week may be one or more.
And S200, acquiring a capacity configuration constraint condition of the energy storage system. And obtaining a capacity configuration optimization model of the energy storage system according to the comprehensive profit optimization function and the capacity configuration constraint condition.
And S300, solving a capacity configuration optimization model of the energy storage system by taking the capacity configuration information as a target to obtain the capacity configuration information of the energy storage system.
In one embodiment, the energy storage system comprises an electrolysis cell, a hydrogen storage tank and a fuel cell, and the capacity configuration information of the energy storage system comprises capacity information of the electrolysis cell, capacity information of the hydrogen storage tank and capacity information of the fuel cell.
In one embodiment, the capacity information of the electrolytic cell comprises a maximum power of the electrolytic cell. The capacity information of the hydrogen storage tank includes a maximum volume of the hydrogen storage tank. The capacity information of the fuel cell includes a maximum power of the fuel cell.
S400, configuring the capacity in the energy storage system according to the capacity configuration information.
The step of configuring the capacity in the energy storage system according to the capacity configuration information includes selecting the electrolyzer, the hydrogen storage tank and the fuel cell under the capacity information of the hydrogen storage tank to configure the energy system.
According to the energy storage capacity configuration method provided by the embodiment of the application, the comprehensive profit optimization function of the energy storage system is obtained according to the profit of the energy storage system running at a typical period from power supply to a power grid, the profit of hydrogen supply to a hydrogen load, the profit of power supply to an offshore oil field load, the profit of hydrogen supply to a gas turbine in the offshore oil field and the construction and maintenance cost. The capacity allocation optimization model includes the synthetic revenue optimization function and the capacity allocation constraints. The energy storage capacity configuration method obtains the capacity configuration information of the energy storage system by solving a capacity configuration optimization model of the energy storage system, and obtains optimized capacity configuration information.
In addition, the energy storage capacity configuration method fully considers the influences of offshore oil field load and a gas turbine power part in an offshore oil field, and the capacity configuration of the energy storage system is better.
Referring collectively to fig. 2, in one embodiment, the step of obtaining a comprehensive profit optimization function of the energy storage system according to the profit of power supply to the power grid, the profit of hydrogen supply to the hydrogen load, the profit of power supply to the offshore oil field load, the profit of hydrogen supply to the gas turbine in the offshore oil field, and the construction and maintenance costs of the energy storage system operating in the typical cycle in step S100 includes:
and S110, obtaining the annual average operation income of the energy storage system according to the power supply income to the power grid, the hydrogen supply income to the hydrogen load, the power supply income to the offshore oil field load and the hydrogen supply income to the gas turbine in the offshore oil field in typical operation.
And S120, obtaining the comprehensive income optimization function according to the annual average operation income and the construction and maintenance cost.
The comprehensive profit optimization function is:
R＝max(Z-ACCAP-COM)
wherein R is the maximum annual average net gain. And Z is the annual average operating yield of the energy storage system and is obtained through operation optimization.
ACCAPIs the annual average investment cost of the system. COMIs the annual average operating maintenance cost of the system.
Table 1 shows the meanings of the symbols referred to in the present application.
TABLE 1
The initial investment cost of the energy storage system is as follows:
wherein, Ci(i ═ 1, 2, 3) are initial investment costs for the electrolyzer, fuel cell, and hydrogen storage tank, respectively. C1Is the initial investment cost of the electrolytic cell. C2Is the initial capital cost of the fuel cell. C3Which is the initial investment cost of the hydrogen storage tank. CCAPFor the initial investment cost (cost of construction period investment).
The annual average investment cost is respectively as follows:
r is the annual aging rate and is taken as 0.05; and m is the operation year, and is taken as 20 years.
C1＝Pelymaxeely
C2＝Pfcmaxefc
C3＝Ptanmaxetan
The annual average operation and maintenance cost is as follows:
COM＝aCely+bCfc+cCtan
wherein a is the percentage of the annual average operation and maintenance cost of the electrolytic cell in the initial investment; b is the percentage of the annual average operation and maintenance cost of the fuel cell in the initial investment; and c is the percentage of the annual average operation and maintenance cost of the hydrogen storage tank to the initial investment.
In one embodiment, a, b, and c all take 0.05.
Referring to fig. 3, in an embodiment, the step of obtaining the annual average operation profit of the energy storage system according to the power supply profit to the power grid, the hydrogen supply profit to the hydrogen load, the power supply profit to the offshore oil field load and the hydrogen supply profit to the gas turbine in the offshore oil field in S110 includes:
and S111, optimizing the operation of the energy storage system in a typical week to obtain the power supply income to the power grid, the hydrogen supply income to the hydrogen load, the power supply income to the offshore oil field load and the hydrogen supply income to the gas turbine in the offshore oil field, so as to obtain the maximum operation income.
The formula for the maximum operating yield for a typical week is:
where n represents the number of typical weeks. InnThe capacity configuration representing the set of energy storage systems corresponds to a maximum weekly operating gain among a plurality of weekly operating gains for the nth week. K represents the total operating yield for a set of capacity configurations for the energy storage system for N weeks.
The objective function of the weekly operation income is as follows:
wherein T is a time point, one point is taken every 1 hour, and the total T is 168 points in one week. InnThe capacity configuration representing the set of energy storage systems corresponds to a maximum weekly operating gain among a plurality of weekly operating gains for the nth week. Because of the plurality of parameters and the plurality of boundary conditions, the plurality of parameters can take different parameter values in the boundary conditions to obtain a plurality of parameter groups. Corresponding to the capacity configuration of a group of energy storage systems, a plurality of parameter groups are substituted to the right of the equal sign of the formula, and then a plurality of operating profits In a week are obtainednRepresents the maximum value of the operating income of a plurality of weeks, and In is obtained by optimizationn。
And S112, obtaining the annual average operation income according to the maximum operation income.
The annual average operating income formula is as follows:
where K represents the sum of the maximum operating total revenue for a set of capacity configurations of the energy storage system for a plurality of typical weeks. And Z is the annual average operating yield.
The step of S111 includes:
s1111, acquiring the power supply income to the power grid, the hydrogen supply income to the hydrogen load, the power supply income to the offshore oil field load, the hydrogen supply income to the gas turbine in the offshore oil field and the capacity configuration constraint condition of the energy storage system, which correspond to the capacity configuration information of a group of energy storage systems and operate in each typical week;
s1112, obtaining a formula of bringing the maximum operation income into the weekly operation income according to the power supply income to the power grid, the hydrogen supply income to the hydrogen load, the power supply income to the offshore oil field load and the hydrogen supply income to the gas turbine in the offshore oil field corresponding to each typical week, and obtaining the weekly operation income of each typical week;
and S1113, overlapping the operation income of each typical week to obtain the operation total income.
And S1114, acquiring a plurality of parameter sets according to the capacity configuration constraint conditions of the energy storage systems, executing the steps S1111 to S1113 corresponding to each group of parameters, respectively calculating a plurality of total operating gains corresponding to the capacity configuration information of one group of energy storage systems, and acquiring a maximum value of the plurality of total operating gains corresponding to the capacity configuration of one group of energy storage systems, namely the maximum operating gain K of a typical week corresponding to the capacity configuration of one group of energy storage systems.
S1114 may be solved by a GUROBI solver, and after the step S112, the energy storage capacity configuration method further includes:
and configuring a corresponding annual average operation income formula for the capacity of the energy storage systems according to the annual average operation income formula. The capacity configuration of the multiple groups of energy storage systems corresponds to the operation income of multiple years. And then the net income of a plurality of annual average can be obtained according to the comprehensive income optimization function.
And taking the maximum value in the annual average net gains, namely the maximum annual average net gain R, and finding the capacity configuration of the group of energy storage systems corresponding to the maximum annual average net gain R.
In one embodiment, the capacity configuration constraints include: the system comprises an offshore wind power active power balance constraint formula, an offshore oil field load active power balance constraint formula, a gas turbine maximum output constraint formula, a gas turbine minimum output constraint formula, a hot standby constraint formula, a gas turbine fuel constraint formula, an electrolysis cell power constraint formula, a fuel cell power constraint formula, an electrolysis cell and fuel cell efficiency constraint formula, a hydrogen load demand constraint formula or a gas storage tank volume constraint formula. And the capacity configuration constraint condition is a boundary condition and is used for ensuring that the energy storage system can normally operate.
The constraints are as follows:
(1) active power balance constraint of offshore wind power
Pwnet，t+Pely，t+Pwload，t＝Pw，t
(2) Offshore oilfield load active power balance constraint
The total output (total power) of the N gas turbines cannot be greater than the offshore field load requirement, which is to prevent the unreasonable situation where gas turbines are sending power onshore.
(3) Maximum and minimum output constraints for gas turbine
Ii，tPgtmin，i≤Pgti，t≤Ii，tPgtmax，i
Ii，tWhen the value is 1, the gas turbine is started in a period t; i isi，tWhen equal to 0, it represents that the gas turbine is shut down for time t.
(4) Hot standby restraint
The full spare capacity of the system is provided by the gas turbine (h takes 0.05):
(5) fuel containment for gas turbine engine
On the right side of equation (5) is the volume of hydrogen consumed by the gas turbine in terms of natural gas over time t.
(6) Cell power constraint
mtPelymin≤Pely,t≤Pelymaxmt
In the formula Pelymin＝0.15Pelymax；m t1 represents cell start-up; m ist0 represents the cell outage.
(7) Fuel cell power constraints
0≤Pfc1,t≤Pfcmax
0≤Pfc2,t≤Pfcmax
Pfc1,t+Pfc2,t≤Pfcmax
(8) Efficiency constraints for electrolyzers and fuel cells
Pely,t＝ηelyVhe,t
Pfc1,t＝ηfcVhf1,t
Pfc2,t＝ηfcVhf2,t
(9) Hydrogen load demand constraints
0≤Vhsell,t≤Vhload,t
(10) Volume constraint of gas storage tank
Vh,t+1＝Vh,t+Vhe,tΔt-Vhf1,tΔt-Vhf2,tΔt-Vhgt,tΔt-Vhsell,tΔt
0≤Vh,t≤Vhmax
In one embodiment, the energy storage capacity configuration method further includes:
and S010, obtaining the power supply income to the power grid according to the direct on-line power of the offshore wind power, the power generation on-line power of the fuel cell, the on-line electricity price and the transmission loss coefficient.
The formula of the power supply income to the power grid is as follows:
t is expressed as time points, one point every 1 hour, and a total of 168 points in a week.
In one embodiment, the energy storage capacity configuration method further includes:
and S020, obtaining the hydrogen supply income to the hydrogen load according to the hydrogen supply amount and the hydrogen selling price of the hydrogen load.
The formula for supplying hydrogen to the hydrogen load is as follows:
t is expressed as time points, one point every 1 hour, and a total of 168 points in a week.
In one embodiment, the energy storage capacity configuration method further includes:
and S030, obtaining the load power supply income to the offshore oil field according to the offshore load power supplied by offshore wind power, the offshore load power supplied by a fuel cell and the price of the power supplied to the offshore load.
The formula of the power supply income to the offshore oilfield load is as follows:
in one embodiment, the energy storage capacity configuration method further includes:
and S040, obtaining the hydrogen supply income of the gas turbine in the offshore oil field according to the amount of hydrogen supplied to the gas turbine, the selling price of the natural gas and the ratio of the unit volume heat value of the hydrogen to the unit volume heat value of the natural gas.
The formula for the hydrogen supply income to the gas turbine in the offshore oil field is as follows:
wherein muhtcAbout 10.659/35.544; mu.slossTaking 0.04; ke2＝0.35Kc。
In an embodiment, the step of solving the capacity configuration optimization model of the energy storage system with the capacity configuration information as a target in S300 to obtain the capacity configuration information of the energy storage system includes:
and solving a capacity configuration optimization model of the energy storage system by adopting a particle swarm optimization with capacity configuration information as a target to obtain optimal capacity configuration information of the energy storage system, and taking the optimal capacity configuration information as the capacity configuration information of the energy storage system.
The core of the particle swarm optimization for solving the optimal solution can be characterized as a process for finding the optimal solution through cooperation and information sharing among individuals in a group.
Referring to fig. 4, in an embodiment, in the process of solving the optimal solution by using the particle swarm optimization, the input target parameters include: capacity information x1 of the electrolytic cell, capacity information x2 of the fuel cell, and capacity information x3 of the hydrogen storage tank. In this example, there are m groups. And meanwhile, various costs can be obtained according to the capacity. The various costs include: the annual average investment cost of the electrolytic cell C1, the annual average investment cost of the fuel cell C2, the annual average investment cost of the hydrogen storage tank C3 and the total annual average operation and maintenance cost COM. And inputting m groups of x1, x2 and x3 into the lower-layer optimization model respectively. The input auxiliary parameters include: inputting hydrogen selling price, net surfing electricity price, offshore load electricity price and natural gas selling price; wind power output (corresponding to P) of each typical cyclew,tEach week is different) and offshore field load curves for each typical week; the hydrogen production efficiency of the electrolytic cell and the power generation efficiency of the fuel cell. These values may also be constant when the energy storage system is determined.
The process of solving the optimal solution by adopting the particle swarm optimization comprises the following steps:
and S1, inputting a group of capacity configuration information into the particle swarm optimization model to obtain the maximum operation benefit of the typical week.
And S2, obtaining annual average operation income according to the maximum operation income.
And S3, obtaining the annual average net income according to the annual average operation income, the annual average operation maintenance cost and the annual average investment cost.
And S4, calculating multiple groups of capacity configuration information according to S1-S3 to obtain multiple annual average net benefits, and obtaining the maximum value in the multiple annual average net benefits to obtain the maximum annual average net benefits.
The process of solving the optimal solution by adopting the particle swarm optimization comprises two-stage optimization: and optimizing the corresponding group of capacity configuration information to obtain the maximum operation income of a typical week. And optimizing corresponding to a plurality of groups of capacity configuration information to obtain the maximum annual average net gain.
After the process of solving the optimal solution by the particle swarm algorithm is finished, the obtained output result can be the optimal capacity configured by the energy storage system, namely the capacity of the electrolytic cell, the capacity of the hydrogen storage tank and the capacity of the fuel cell.
The process of solving the optimal solution is actually a unit combination problem, and a Gurobi solver is used for solving the optimal solution. The weekly operation profit per typical week can be obtained corresponding to a set of target parameters (capacity information of the electrolyzer, capacity information of the hydrogen storage tank, and capacity information of the fuel cell), and further the total operating profit of N typical weeks can be obtained.
In one embodiment, the particle swarm algorithm further comprises:
the profit of N weeks is equivalent to the profit of 1 year, and m groups of annual net profits are obtained by combining the cost.
And taking the annual net profit as an adaptive value of the particle swarm, and judging whether the optimal adaptive value meets the precision condition.
And outputting the optimal capacity if the precision condition is met. If not, the individual extrema and the overall extrema are updated and the positions and velocities of all the particles are updated.
The parameters specifically set for the particle swarm algorithm include: the method comprises the following steps of particle swarm scale, particle swarm dereferencing range, maximum iteration algebra, maximum cycle times and precision conditions.
In a specific embodiment, the size of the population of particles is set to 25, i.e. there are 25 groups (x1, x2, x3) of capacity in a generation. The size of the particle population affects the accuracy of the speed of computational convergence. Too large a scale may be too slow to compute and too small a scale may not converge accurately, which is chosen empirically.
The value range of the particle group refers to the value ranges of x1, x2 and x 3. The value range of the particle swarm influences whether convergence occurs and the convergence speed. If the optimal value is not within the range, the optimization result will appear on the boundary. The smaller the value range, the faster the convergence rate. This range is also selected primarily by manual experience.
The maximum iteration algebra is set to 30. And the calculation is stopped by circulating to 30 generations. The maximum iteration algebra is selected mainly according to the complexity of the problem, and the precision can be achieved in 30 generations of the problem in the embodiment.
The maximum cycle number refers to the fact that when the total optimal value of continuous generations of calculation is smaller than the precision condition, the calculation is stopped after convergence is indicated. In one embodiment, 8 is set, and the calculation is stopped when the variation of the continuous 8-generation optimal adaptation value is smaller than the precision condition.
The accuracy condition is related to the value of the fitness function, which is a problem where the fitness value is of the order of 107The accuracy is set to 1000. Namely when the change of the continuous 8-generation optimal adaptation value is less than one per thousand, the problem is converged.
In the particle swarm optimization, manual updating is not needed, and parameters of the function which can be automatically updated comprise individual extrema, overall extrema and positions and speeds of particles:
individual extrema: the optimal adaptation value (annual net profit) in different generations for each particle.
Total extrema: and (4) optimal adaptive values in different generations of 25 particles.
Position and velocity of the particle: iterative updating of the value of each particle (x1, x2, x3) is performed to bring (x1, x2, x3) closer to the extreme position.
It should be understood that, although the steps in the flowchart of fig. 4 are shown in order as indicated by the arrows, the steps are not necessarily performed in order as indicated by the arrows. The steps are not performed in the exact order shown and described, and may be performed in other orders, unless explicitly stated otherwise. Moreover, at least a portion of the steps in fig. 4 may include multiple sub-steps or multiple stages that are not necessarily performed at the same time, but may be performed at different times, and the order of performance of the sub-steps or stages is not necessarily sequential, but may be performed in turn or alternately with other steps or at least a portion of the sub-steps or stages of other steps. For specific definition of the energy storage capacity configuration device, reference may be made to the above definition of the energy storage capacity configuration method, which is not described herein again. The modules in the energy storage capacity configuration device in the computer equipment can be wholly or partially realized by software, hardware and a combination thereof. The modules can be embedded in a hardware form or independent from a processor in the computer device, and can also be stored in a memory in the computer device in a software form, so that the processor can call and execute operations corresponding to the modules.
In one embodiment, the method for optimizing the operation and configuration of the offshore wind power-hydrogen energy combined system is verified by using the output and load data of a certain wind power plant, and the specific application method is as follows:
the grid tariff and the gas turbine parameters are the same in the operational and configuration optimization. On-line electricity price Ke1,tPeak to valley electricity rates were used as shown in table 2.
TABLE 2
| Type (B) | Time period | Electricity price (Yuan/kWh) |
| Time of peak | 8:00-12:00，15:00-22:00 | 0.891 |
| At ordinary times | 6:00-8:00，12:00-15:00，22:00-23:00 | 0.510 |
| At grain time | 23:00-24:00，0:00-6:00 | 0.150 |
5 gas turbines were used as backup for the offshore load, the parameters of which are shown in Table 3.
TABLE 3
| Gas turbine numbering | Pgtmin,i(MW) | Pgtmax,i(MW) | bi(Nm3/MWh) | ci(Nm3/h) |
| 1-3 | 3 | 10.5 | 320 | 152.6 |
| 4-5 | 1 | 5 | 343 | 130.7 |
(1) Operation optimization of offshore wind power-hydrogen energy combined system
The other parameters of the run optimization are shown in table 4. Hydrogen demand Vhload,tSet to 24000Nm3And selling in 24 per day.
TABLE 4
| Parameter(s) | Set value | Parameter(s) | Set value |
| ηely(kwh/Nm3) | 4.82 | Pelymax(MW) | 60 |
| ηfc(kwh/Nm3) | 1.6 | Pfcmax(MW) | 20 |
| Kc(Yuan/Nm)3) | 3 | Vhmax(Nm3) | 20*1000/0.089 |
| Kh(Yuan/Nm)3) | 4 | Ke2(Yuan/kWh) | 1.05 |
The final optimization yields a system in which the power of the electrolyzer, fuel cell and gas turbine is shown in figure 1 and the distribution of hydrogen energy is shown in figure 5. The parameters adopted by the configuration optimization of the energy storage capacity configuration method are shown in fig. 6. The hydrogen used in the gas turbine in fig. 6 is 0. However, when the price of hydrogen becomes low and direct sale is not cost effective, the gas turbine is distributed with hydrogen. Another situation is that the price of natural gas becomes high and it is not cost effective for the gas turbine to burn natural gas, but hydrogen is also distributed to the gas turbine.
In a typical week, the operating yield of the system after adding the electrolyzer-hydrogen storage tank-fuel cell increased 75.02 ten thousand yuan compared to the original system.
(2) Configuration optimization of offshore wind power-hydrogen energy combined system
The parameters used for configuration optimization are shown in table 5. And taking 2 as N, and replacing the annual income by using one big wind week and one small wind week. The final configuration optimization results are that the electrolytic cell is 11.4MW, the fuel cell is 0MW, and the hydrogen storage tank is 2.6 t.
TABLE 5
| Parameter(s) | Set value | Parameter(s) | Set value |
| eely(Yuan/kw) | 8000 | ηely(kwh/Nm3) | 4.82 |
| efc(Yuan/kw) | 9000 | ηfc(kwh/Nm3) | 1.6 |
| etan(Yuan/kg) | 4800 | Vh,load(Nm3Day) | 30000 |
| Kh(Yuan/Nm)3) | 4 | Kc(Yuan/Nm)3) | 3 |
The embodiment of the application provides an energy storage capacity configuration device, which comprises a first acquisition module, a second acquisition module, a solving module and a configuration module.
The first acquisition module is used for acquiring the energy storage system's income to the power supply of electric wire netting, to hydrogen load hydrogen supply income, to offshore oil field load power supply income, to the gas turbine in the offshore oil field hydrogen supply income, construction maintenance cost, and according to energy storage system's income to the power supply of electric wire netting, to hydrogen load hydrogen supply income, to offshore oil field load power supply income, to the gas turbine in the offshore oil field hydrogen supply income and construction maintenance cost obtain energy storage system's comprehensive income optimization function.
The second obtaining module is used for obtaining a capacity configuration constraint condition of the energy storage system and obtaining a capacity configuration optimization model of the energy storage system according to the comprehensive profit optimization function and the capacity configuration constraint condition.
The solving module is used for solving the capacity configuration optimization model of the energy storage system by taking the capacity configuration information as a target to obtain the capacity configuration information of the energy storage system.
The configuration module is used for configuring the capacity of the energy storage system according to the capacity configuration information.
In one embodiment, the first obtaining module includes a first obtaining sub-module, a second obtaining sub-module, and a third obtaining sub-module.
The first obtaining submodule is used for obtaining annual average operation income of the energy storage system according to the power supply income to the power grid, the hydrogen supply income to the hydrogen load, the power supply income to the offshore oil field load and the hydrogen supply income to the gas turbine in the offshore oil field in typical operation. And obtaining the comprehensive income optimization function according to the annual average operation income and the construction and maintenance cost.
And the second obtaining submodule is used for obtaining the comprehensive income optimization function according to the annual average operation income and the construction and maintenance cost.
And the third obtaining submodule is used for obtaining a capacity configuration constraint condition of the energy storage system according to the capacity configuration of the energy storage system and the operation coefficient of the energy storage system in the operation process.
In one embodiment, the first obtaining sub-module includes a first step module and a second step module.
The first step-by-step module is used for obtaining weekly operation income according to typical weekly operation income from power supply to a power grid, income from hydrogen supply to a hydrogen load, income from power supply to an offshore oil field load and income from hydrogen supply to a gas turbine in the offshore oil field.
And the second step module is used for obtaining the annual average operation income according to the weekly operation income.
In one embodiment, the energy storage capacity configuration apparatus further comprises a first revenue module, a second revenue module, a third revenue module, and a fourth revenue module.
The first income module is used for obtaining the power supply income for the power grid according to the direct on-line power of the offshore wind power, the power generation on-line power of the fuel cell, the on-line electricity price and the transmission loss coefficient.
And the second profit module is used for obtaining the profit of supplying hydrogen to the hydrogen load according to the amount of the hydrogen supplied to the hydrogen load and the selling price of the hydrogen.
And the third profit module is used for obtaining the profit of supplying hydrogen to the gas turbine in the offshore oil field according to the offshore wind power supplied offshore load power, the fuel cell supplied offshore load power and the price of the supplied offshore load electricity.
And the fourth profit module is used for obtaining the hydrogen supply profit of the gas turbine in the offshore oil field according to the hydrogen supply load, the selling price of the natural gas and the ratio of the unit volume heat value of the hydrogen and the natural gas.
In one embodiment, the solving module is configured to solve the capacity configuration optimization model of the energy storage system by using a machine learning algorithm with the capacity configuration information as a target to obtain the capacity configuration information of the energy storage system.
In one embodiment, the solving module is configured to solve the capacity configuration optimization model of the energy storage system by using a particle swarm algorithm with capacity configuration information as a target to obtain optimal capacity configuration information of the energy storage system, and use the optimal capacity configuration information as the capacity configuration information of the energy storage system.
The embodiment of the present application provides a computer device, which includes a memory and a processor, where the memory stores a computer program that can be executed on the processor, and the processor executes the computer program to implement the steps of the energy storage capacity configuration method according to any of the above embodiments.
The computer device comprises a processor, a memory, a network interface, a display screen and an input system which are connected through a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device includes a non-volatile readable storage medium, an internal memory. The non-transitory readable storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of an operating system and computer programs in the non-volatile readable storage medium. The network interface of the computer device is used for communicating with an external terminal through a network connection. The computer program is executed by a processor to implement a method of energy storage capacity configuration. The display screen of the computer equipment can be a liquid crystal display screen or an electronic ink display screen, and the input system of the computer equipment can be a touch layer covered on the display screen, a key, a track ball or a touch pad arranged on the shell of the computer equipment, an external keyboard, a touch pad or a mouse and the like.
The processor, when executing the computer program, implements the steps of:
and acquiring the energy storage system's income from supplying power to the power grid, income from supplying hydrogen to the hydrogen load, income from supplying power to the offshore oil field load, income from supplying hydrogen to the gas turbine in the offshore oil field, and construction and maintenance cost, and acquiring the energy storage system's comprehensive income optimization function according to the energy storage system's income from supplying power to the power grid, income from supplying hydrogen to the hydrogen load, income from supplying power to the offshore oil field load, income from supplying hydrogen to the gas turbine in the offshore oil field, and the construction and maintenance cost.
And acquiring a capacity configuration constraint condition of the energy storage system, and acquiring a capacity configuration optimization model of the energy storage system according to the comprehensive profit optimization function and the capacity configuration constraint condition.
And solving a capacity configuration optimization model of the energy storage system by taking the capacity configuration information as a target to obtain the capacity configuration information of the energy storage system.
And configuring the capacity in the energy storage system according to the capacity configuration information.
The present application provides a readable storage medium, on which a computer program is stored, where the computer program is executed by a processor to implement the steps of the energy storage capacity configuration method according to any of the above embodiments.
The computer program when executed by a processor implements the steps of:
and acquiring the energy storage system's income from supplying power to the power grid, income from supplying hydrogen to the hydrogen load, income from supplying power to the offshore oil field load, income from supplying hydrogen to the gas turbine in the offshore oil field, and construction and maintenance cost, and acquiring the energy storage system's comprehensive income optimization function according to the energy storage system's income from supplying power to the power grid, income from supplying hydrogen to the hydrogen load, income from supplying power to the offshore oil field load, income from supplying hydrogen to the gas turbine in the offshore oil field, and the construction and maintenance cost.
And acquiring a capacity configuration constraint condition of the energy storage system, and acquiring a capacity configuration optimization model of the energy storage system according to the comprehensive profit optimization function and the capacity configuration constraint condition.
And solving a capacity configuration optimization model of the energy storage system by taking the capacity configuration information as a target to obtain the capacity configuration information of the energy storage system.
And configuring the capacity in the energy storage system according to the capacity configuration information.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by hardware instructions of a computer program, which can be stored in a non-volatile computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. Any reference to memory, storage, database, or other medium used in the embodiments provided herein may include non-volatile and/or volatile memory, among others. Non-volatile memory can include read-only memory (ROM), Programmable ROM (PROM), Electrically Programmable ROM (EPROM), Electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), Dynamic RAM (DRAM), Synchronous DRAM (SDRAM), Double Data Rate SDRAM (DDRSDRAM), Enhanced SDRAM (ESDRAM), Synchronous Link DRAM (SLDRAM), Rambus Direct RAM (RDRAM), direct bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).
The technical features of the embodiments described above may be arbitrarily combined, and for the sake of brevity, all possible combinations of the technical features in the embodiments described above are not described, but should be considered as being within the scope of the present specification as long as there is no contradiction between the combinations of the technical features.
The above-described examples merely represent several embodiments of the present application and are not to be construed as limiting the scope of the claims. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the concept of the present application, which falls within the scope of protection of the present application. Therefore, the protection scope of the present patent shall be subject to the appended claims.
Claims (13)
1. A method of energy storage capacity allocation, comprising:
acquiring power supply income to a power grid, hydrogen supply income to a hydrogen load, power supply income to an offshore oil field load, hydrogen supply income to a gas turbine in an offshore oil field and construction and maintenance costs of the energy storage system in typical weekly operation, and acquiring a comprehensive income optimization function of the energy storage system according to the power supply income to the power grid, the hydrogen supply income to the hydrogen load, the power supply income to the offshore oil field load, the hydrogen supply income to the gas turbine in the offshore oil field and the construction and maintenance costs of the energy storage system in typical weekly operation;
acquiring a capacity configuration constraint condition of the energy storage system, and obtaining a capacity configuration optimization model of the energy storage system according to the comprehensive profit optimization function and the capacity configuration constraint condition;
solving a capacity configuration optimization model of the energy storage system by taking the capacity configuration information as a target to obtain the capacity configuration information of the energy storage system;
and configuring the capacity in the energy storage system according to the capacity configuration information.
2. The method of energy storage capacity allocation according to claim 1, wherein the step of deriving a comprehensive profit optimization function for the energy storage system based on the gains in power supply to the grid, the gains in hydrogen supply to the hydrogen load, the gains in power supply to the offshore field load, the gains in hydrogen supply to the gas turbine in the offshore field, and the costs of construction and maintenance for the energy storage system in typical operation cycles comprises:
obtaining annual average operation income of the energy storage system according to the income of power supply to a power grid, the income of hydrogen supply to a hydrogen load, the income of power supply to a load of the offshore oil field and the income of hydrogen supply to a gas turbine in the offshore oil field in typical operation period;
and obtaining the comprehensive income optimization function according to the annual average operation income and the construction and maintenance cost.
3. The method of energy storage capacity allocation according to claim 2, wherein the step of deriving an annual average operating yield of the energy storage system from the power grid supply revenue, the hydrogen load supply revenue, the offshore field load supply revenue, and the gas turbine hydrogen load supply revenue for the offshore field based on typical weekly operation comprises:
obtaining maximum operation income according to the income of power supply to a power grid, the income of hydrogen supply to a hydrogen load, the income of power supply to a load of an offshore oil field and the income of hydrogen supply to a gas turbine in the offshore oil field in typical operation period;
and obtaining the annual average operation income according to the maximum operation income.
4. The energy storage capacity configuration method of claim 1, wherein the capacity configuration constraints comprise: the system comprises an offshore wind power active power balance constraint formula, an offshore oil field load active power balance constraint formula, a gas turbine maximum output constraint formula, a gas turbine minimum output constraint formula, a hot standby constraint formula, a gas turbine fuel constraint formula, an electrolysis cell power constraint formula, a fuel cell power constraint formula, an electrolysis cell and fuel cell efficiency constraint formula, a hydrogen load demand constraint formula or a gas storage tank volume constraint formula.
5. The energy storage capacity configuration method of claim 1, further comprising:
and obtaining the power supply income for the power grid according to the direct on-line power of the offshore wind power, the power generation on-line power of the fuel cell, the on-line electricity price and the transmission loss coefficient.
6. The energy storage capacity configuration method of claim 1, further comprising:
and obtaining the hydrogen supply income to the hydrogen load according to the hydrogen supply amount of the hydrogen load and the hydrogen selling price.
7. The energy storage capacity configuration method of claim 1, further comprising:
and obtaining the power supply income for the offshore oilfield load according to the offshore wind power supply offshore load power, the fuel cell supply offshore load power and the supply offshore load price.
8. The energy storage capacity configuration method of claim 1, further comprising:
and obtaining the hydrogen supply income of the gas turbine in the offshore oil field according to the hydrogen supply load, the selling price of the natural gas and the ratio of the unit volume heat value of the hydrogen and the natural gas.
9. The energy storage capacity configuration method according to claim 1, wherein the step of solving the capacity configuration optimization model of the energy storage system with the capacity configuration information as a target to obtain the capacity configuration information of the energy storage system comprises:
and solving a capacity configuration optimization model of the energy storage system by adopting a particle swarm optimization with capacity configuration information as a target to obtain optimal capacity configuration information of the energy storage system, and taking the optimal capacity configuration information as the capacity configuration information of the energy storage system.
10. The energy storage capacity configuration method according to claim 1, wherein the energy storage system comprises an electrolyzer, a hydrogen storage tank and a fuel cell, and the capacity configuration information of the energy storage system comprises capacity information of the electrolyzer, capacity information of the hydrogen storage tank and capacity information of the fuel cell.
11. An energy storage capacity configuration device, comprising:
the first acquisition module is used for acquiring the power supply income of the energy storage system to a power grid, the hydrogen supply income to a hydrogen load, the power supply income to a load of an offshore oil field, the hydrogen supply income to a gas turbine in the offshore oil field and the construction and maintenance cost, and acquiring a comprehensive income optimization function of the energy storage system according to the power supply income of the energy storage system to the power grid, the hydrogen supply income to the hydrogen load, the power supply income to the load of the offshore oil field, the hydrogen supply income to the gas turbine in the offshore oil field and the construction and maintenance cost;
the second obtaining module is used for obtaining a capacity configuration constraint condition of the energy storage system and obtaining a capacity configuration optimization model of the energy storage system according to the comprehensive profit optimization function and the capacity configuration constraint condition;
the solving module is used for solving a capacity configuration optimization model of the energy storage system by taking the capacity configuration information as a target to obtain the capacity configuration information of the energy storage system;
and the configuration module is used for configuring the capacity of the energy storage system according to the capacity configuration information.
12. A computer device comprising a memory, a processor, the memory having stored thereon a computer program operable on the processor, wherein the processor, when executing the computer program, performs the steps of the energy storage capacity configuration method of any of claims 1 to 10.
13. A readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the steps of the energy storage capacity configuration method according to any one of claims 1 to 10.
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